The Utility Rate Database (URDB) is a free storehouse of rate structure information from utilities in the United States. Here, you can search for your utilities and rates to find out exactly how you are charged for your electric energy usage. Understanding this information can help reduce your bill, for example, by running your appliances during off-peak hours (times during the day when electricity prices are less expensive) and help you make more informed decisions regarding your energy usage.

Rates are also extremely important to the energy analysis community for accurately determining the value and economics of distributed generation such as solar and wind power. In the past, collecting rates has been an effort duplicated across many institutions. Rate collection can be tedious and slow, however, with the introduction of the URDB, OpenEI aims to change how analysis of rates is performed. The URDB allows anyone to access these rates in a computer-readable format for use in their tools and models. OpenEI provides an API for software to automatically download the appropriate rates, thereby allowing detailed economic analysis to be done without ever having to directly handle complex rate structures. Essentially, rate collection and processing that used to take weeks or months can now be done in seconds!

NREL’s System Advisor Model (formerly Solar Advisor Model or SAM), currently has the ability to communicate with the OpenEI URDB over the internet. SAM can download any rate from the URDB directly into the program, thereby enabling users to conduct detailed studies on various power systems ranging in size from a small residential rooftop solar system to large utility scale installations. Other applications available at NREL, such as OpenPV and IMBY, will also utilize the URDB data.

Upcoming features include better support for entering net metering parameters, maps to summarize the data, geolocation capabilities, and hundreds of additional rates!

This dataset, compiled by NREL using data from ABB, the Velocity Suite (http://energymarketintel.com/) and the U.S. Energy Information Administration dataset 861 (http://www.eia.gov/electricity/data/eia861/), provides average residential, commercial and industrial electricity rates with likely zip codes for both investor owned utilities (IOU) and non-investor owned utilities. Note: the files include average rates for each utility (not average rates per zip code), but not the detailed rate structure data found in the OpenEI U.S. Utility Rate Database (https://openei.org/apps/USURDB/).

Abstract

This dataset, compiled by NREL using data from ABB, the Velocity Suite and the U.S. Energy Information Administration dataset 861, provides average residential, commercial and industrial electricity rates with likely zip codes for both investor owned utilities (IOU) and non-investor owned utilities. Note: the files include average rates for each utility (not average rates per zip code), but not the detailed rate structure data found in the OpenEI U.S. Utility Rate Database.

The table Non IOU Zipcodes 2019 is part of the dataset Open Energy Data Initiative: U.S. Electric Utility Consumption and Rates ***, available at https://redivis.com/datasets/w5hb-cs453cj2k. It contains 36494 rows across 9 variables.

Analysis of ‘U.S. Electric Utility Companies and Rates: Look-up by Zipcode (2019)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/2fc052b0-4404-425e-92a1-c68a9d5344b8 on 12 February 2022.

--- Dataset description provided by original source is as follows ---

This dataset, compiled by NREL using data from ABB, the Velocity Suite and the U.S. Energy Information Administration dataset 861, provides average residential, commercial and industrial electricity rates with likely zip codes for both investor owned utilities (IOU) and non-investor owned utilities. Note: the files include average rates for each utility (not average rates per zip code), but not the detailed rate structure data found in the OpenEI U.S. Utility Rate Database.

--- Original source retains full ownership of the source dataset ---

This dataset, compiled by NREL using data from Ventyx and the U.S. Energy Information Administration dataset 861, provides average residential, commercial and industrial electricity rates by zip code for both investor owned utilities (IOU) and non-investor owned utilities in Utah. Note: the file includes average rates for each utility, but not the detailed rate structure data found in the OpenEI U.S. Utility Rate Database. A more recent version of this data is also available through the NREL Utility Rate API with more search options. This data was released by NREL/Ventyx in February 2011.

Spreadsheet with water rate information, including average residential monthly water bill, average residential monthly wastewater bill and water and wastewater rate at a consumption level of 5,000 gallons, volumetric and fixed rates, rate type, billing cycles, number and width of rate blocks, for each utility for which data was available in the northeastern Illinois region. Data is presented in original and standardized formats, each with a separate key. Fiscal year 2019 ran from July 2018 to June 2019.FY2009 DataFY2015 DataFY2017 DataFY2021 Data

This spreadsheet contains information reported by over 200 investor-owned utilities to the Federal Energy Regulatory Commission in the annual filing FERC Form 1 for the years 1994-2019. It contains 1) annual capital costs for new transmission, distribution, and administrative infrastructure; 2) annual operation and maintenance costs for transmission, distribution, and utility business administration; 3) total annual MWh sales and sales by customer class; 4) annual peak demand in MW; and 5) total customer count and the number of customers by class. Annual spending on new capital infrastructure is read from pages 204 to 207 of FERC Form 1, titled Electric Plant in Service. Annual transmission capital additions are recorded from Line 58, Column C - Total Transmission Plant Additions. Likewise, annual distribution capital additions are recorded from Line 75, Column C - Total Distribution Plant Additions. Administrative capital additions are recorded from Line 5, Column C - Total Intangible Plant Additions, and Line 99, Column C - Total General Plant Additions. Operation and maintenance costs associated with transmission, distribution, and utility administration are read from pages 320 to 323 of FERC Form 1, titled Electric Operation and Maintenance Expenses. Annual transmission operation and maintenance are recorded from Line 99, Column B - Total Transmission Operation Expenses for Current Year, and Line 111, Column B - Total Transmission Maintenance Expenses for Current Year. Likewise, annual distribution operation and maintenance costs are recorded from Line 144, Column B - Total Distribution Operation Expenses, and Line 155, Column B - Total Distribution Maintenance Expenses. Administrative operation and maintenance costs are recorded from: Line 164, Column B - Total Customers Accounts Expenses; Line 171, Column B - Total Customer Service and Information Expenses; Line 178, Column B - Total Sales Expenses; and Line 197, Column B - Total Administrative and General Expenses. The annual peak demand in MW over the year is read from page 401, titled Monthly Peaks and Output. The monthly peak demand is listed in Lines 29 to 40, Column D. The maximum of these monthly reports during each year is taken as the annual peak demand in MW. The annual energy sales and customer count data come from page 300, Electric Operating Revenues. The values are provided in Line 2 - Residential Sales, Line 4 - Commercial Sales, Line 5 - Industrial Sales, and Line 10 - Total Sales to Ultimate Consumers. More information about the database is available in an associated report published by the University of Texas at Austin Energy Institute: https://live-energy-institute.pantheonsite.io/sites/default/files/UTAustin_FCe_TDA_2016.pdf Also see an associated paper published in the journal Energy Policy: Fares, Robert L., and Carey W. King. "Trends in transmission, distribution, and administration costs for US investor-owned electric utilities." Energy Policy 105 (2017): 354-362. https://doi.org/10.1016/j.enpol.2017.02.036 All data come from the Federal Energy Regulatory Commission FERC Form 1 Database available in Microsoft Visual FoxPro Format: https://www.ferc.gov/docs-filing/forms/form-1/data.asp

NREL has assembled a list of U.S. retail electricity tariffs and their associated demand charge rates for the Commercial and Industrial sectors. The data was obtained from the Utility Rate Database. Keep the following information in mind when interpreting the data: (1) These data were interpreted and transcribed manually from utility tariff sheets, which are often complex. It is a certainty that these data contain errors, and therefore should only be used as a reference. Actual utility tariff sheets should be consulted if an action requires this type of data. (2) These data only contains tariffs that were entered into the Utility Rate Database. Since not all tariffs are designed in a format that can be entered into the Database, this list is incomplete - it does not contain all tariffs in the United States. (3) These data may have changed since this list was developed (4) Many of the underlying tariffs have additional restrictions or requirements that are not represented here. For example, they may only be available to the agricultural sector or closed to new customers. (5) If there are multiple demand charge elements in a given tariff, the maximum demand charge is the sum of each of the elements at any point in time. Where tiers were present, the highest rate tier was assumed. The value is a maximum for the year, and may be significantly different from demand charge rates at other times in the year. Utility Rate Database: https://openei.org/wiki/Utility_Rate_Database

Spreadsheet with water rate information, including average residential monthly water bill, average residential monthly wastewater bill and water and wastewater rate at a consumption level of 5,000 gallons, volumetric and fixed rates, rate type, billing cycles, number and width of rate blocks, for each utility for which data was available in the northeastern Illinois region. Data is presented in original and standardized formats, each with a separate key. Fiscal year 2009 ran from July 2008 to June 2009.FY2015 DataFY2017 DataFY2019 DataFY2021 Data

The retail price for electricity in the United States stood at an average of 12.72 U.S. dollar cents per kilowatt-hour in 2023. This is the highest figure reported in the indicated period. Nevertheless, the U.S. still has one of the lowest electricity prices worldwide. As a major producer of primary energy, energy prices are lower than in countries that are more reliant on imports or impose higher taxes. Electricity prices in the U.S. by consumer group On average, retail electricity prices in the U.S. grew by over 85 percent since the beginning of the century. However, not every sector has been affected equally by the said price increase. U.S. electricity prices for residential customers saw a much steeper increase in the period, while transportation prices increased by approximately 50 percent. Reasons for increases in electricity prices The rising prices are justified by the costs of power production and power grid maintenance. Although the production cost of electricity generated from coal, natural gas, and nuclear sources remained relatively stable, the integration of renewable energy sources, investments in smart grid technologies, growing peak demand, power blackouts caused by natural disasters, and the global energy crisis in 2022 continued to trouble the electric utility industry in recent years. Average U.S. electricity prices per state can also vary widely, with Hawaii residents experiencing some of the highest rates in the country.

These data underpin an analysis of the time-sensitive impacts of energy efficiency and flexibility measures in the U.S. building sector using Scout (scout.energy.gov), a reproducible and granular model of U.S. building energy use developed by the U.S. national labs for the U.S. Department of Energy's Building Technologies Office.

The analysis applies sub-annual adjustments to U.S. baseline building energy use, cost, and emissions in order to characterize how these metrics vary across hour of the day, season, and geographic region in the U.S. building sector. These adjustments are based on daily energy load, price, and emissions shapes from various data sources and are used to re-apportion baseline energy, cost, and emissions totals from EIA's Annual Energy Outlook (AEO) Reference Case projections across all hours of a year. The resulting sub-annual baselines are specified by building sector, end use, region, and season and can be used in analyses of building efficiency and flexibility measures to quantify their time-sensitive impacts at the national scale. Analyses of these data demonstrate that energy efficiency measures continue to show strong value under a time-sensitive framework while the value of flexibility depends on assumed electricity rates, measure magnitude and duration, and the amount of savings already captured by efficiency.

The data uploaded below include CSV files that show hourly energy use, cost, and emissions totals for the U.S. building sector as well as by end-use, region, and season. An additional CSV includes residential and commercial price intensities (USD/quad) for all hours of the day based on different time-of-use (TOU) rate data from the U.S. Utility Rate Database (URDB). Further detail on each of these CSVs is given below:

• 'TSV_baseline_totals.csv': this file shows hourly total energy, cost, and emissions estimates for commercial and residential buildings in 2018 and 2030. It presents these estimates in Quads (source), Quads (site), and TWh (site). For the cost totals, it presents two estimates for each year and building sector, including one using the median TOU rate from the URDB and one using the average retail rate for the corresponding building sector. For converting source energy to site, total delivered electricity and electricity-related losses data for the residential and commercial sector are drawn from AEO Summary Table A2.
• 'TSV_baseline_end-use.csv': this file shows hourly energy, cost, and emissions estimates for commercial and residential buildings in 2018 and 2030 broken out by building end-use. It presents totals in terms of both source and site energy as above and presents cost totals based on the median TOU rate for each building sector from the URDB.
• 'TSV_baseline_region.csv': this file shows hourly energy, cost, and emissions estimates for commercial and residential space heating and cooling end uses in 2018 and 2030 for each American Institute of Architects (AIA) climate zone. It presents totals in terms of both source and site energy as above and presents cost totals based on the median TOU rate for each building sector from the URDB.
• 'TSV_baseline_region_season.csv': this file shows a similar disaggregation of the data as ‘TSV_baseline_region.csv’, but it further disaggregates results by season. The seasonal definitions are as follows: 'intermediate' (October to November; March to April), 'winter' (November to February), and 'summer' (May to September).
• 'TSV_annual_price_intensities.csv': this file presents annual hourly price intensities for the commercial and residential building sectors in 2018 and 2030 based on different TOU rate data from the URDB. Three different rate structures are included for each building sector, and these are the 5th, 50th, and 95th percentile of all existing commercial and residential TOU rates in the URDB in terms of their peak to off-peak price ratio.

One in a series of studies on customer response to utility regulatory pricing in early 1975, the Arkansas demonstration project was carried out by the Federal Energy Administration (FEA), the Arkansas Public Service Commission, and Torche Ross and Company, spanning 12 months from February 1976 to January 1977. The study was originally titled the Arkansas Demand Management Study and was an experiment to generate and analyze data on the effects of peak-load pricing on residential electricity consumption. The experimental design featured a time of day peak-load pricing test as well as a seasonal pricing test. Five sets of data resulted from the demonstration: questionnaire survey data from the customers, summary demographic information, utility load reports, weather data, and customer usage records. All five sets are available in this data collection. The questionnaire survey data in Part 1 consists of information gathered from a post experimental survey that includes both control and experimental customers. Parts 3-5 each contain 28 days of data, with Parts 3 and 5 including hourly data. Parts 3-5 also contain identifying information that links their data to the pertinent customer/participant's demographic data in Part 2.

The dataset contains information on utility or customer-owned dispersed generation (NOT grid-connected) such as the number, capacity and types of generators.

United States Electric Retail Price: Sold by Electric Utilities: Avg: Residential data was reported at 12.890 USD/kWh in 2017. This records an increase from the previous number of 12.550 USD/kWh for 2016. United States Electric Retail Price: Sold by Electric Utilities: Avg: Residential data is updated yearly, averaging 7.565 USD/kWh from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 12.890 USD/kWh in 2017 and a record low of 2.200 USD/kWh in 1970. United States Electric Retail Price: Sold by Electric Utilities: Avg: Residential data remains active status in CEIC and is reported by Energy Information Administration. The data is categorized under Global Database’s United States – Table US.P011: Electricity Price.

This table groups Austin Energy customers into four classes: residential, commercial, industrial, and lighting (public street/highway and other). View the annual rates in cents per kWh by customer class starting in 2006. Learn more about Austin Energy’s rates at http://austinenergy.com/wps/portal/ae/rates.

Graph and download economic data for Consumer Price Index for All Urban Consumers: Electricity in U.S. City Average (CUSR0000SEHF01) from Jan 1952 to Feb 2025 about electricity, urban, consumer, CPI, price index, indexes, price, and USA.

Summary of utility base rate changes since 1998 for major NYS utilities.

This data collection provides information on characteristics of housing units in 11 selected Metropolitan Statistical Areas (MSAs) of the United States. Although the unit of analysis is the housing unit rather than its occupants, the survey also is a comprehensive source of information on the demographic characteristics of household residents. Data collected include general housing characteristics such as the year the structure was built, type and number of living quarters, occupancy status, presence of commercial establishments on the property, and property value. Data are also provided on kitchen and plumbing facilities, type of heating fuel used, source of water, sewage disposal, and heating and air-conditioning equipment. Questions about housing quality include condition of walls and floors, adequacy of heat in winter, availability of electrical outlets in rooms, basement and roof water leakage, and exterminator service for mice and rats. Data related to housing expenses include mortgage or rent payments, utility costs, fuel costs, property insurance costs, real estate taxes, and garbage collection fees. Variables are also supplied on neighborhood conditions such as quality of roads and presence of crime, trash, litter, street noise, abandoned structures, commercial activity, and odors or smoke, as well as the adequacy of services such as public transportation, schools, shopping facilities, police protection, recreation facilities, and hospitals or clinics. In addition to housing characteristics, data on age, sex, race, marital status, income, and relationship to householder are provided for each household member. Additional data are supplied for the householder, including years of school completed, Spanish origin, and length of residence.

This data collection provides information on the characteristics of a national sample of housing units, including apartments, single-family homes, mobile homes, and vacant housing units in 2009. The data are presented in eight separate parts: Part 1, Home Improvement Record, Part 2, Journey to Work Record, Part 3, Mortgages Recorded, Part 4, Housing Unit Record (Main Record), Recodes (One Record per Housing Unit), and Weights, Part 5, Manager and Owner of Rental Units Record, Part 6, Person Record, Part 7, High Burden Unit Record, and Part 8, Recent Mover Groups Record. Part 1 data include questions about upgrades and remodeling, cost of alterations and repairs, as well as the household member who performed the alteration/repair. Part 2 data include journey to work or commuting information, such as method of transportation to work, length of trip, and miles traveled to work. Additional information collected covers number of hours worked at home, number of days worked at home, average time respondent leaves for work in the morning or evening, whether respondent drives to work alone or with others, and a few other questions pertaining to self-employment and work schedule. Part 3 data include mortgage information, such as type of mortgage obtained by respondent, amount and term of mortgages, as well as years needed to pay them off. Other items asked include monthly payment amount, reason mortgage was taken out, and who provided the mortgage. Part 4 data include household-level information, including demographic information, such as age, sex, race, marital status, income, and relationship to householder. The following topics are also included: data recodes, unit characteristics, and weighting information. Part 5 data include information pertaining to owners of rental properties and whether the owner/resident manager lives on-site. Part 6 data include individual person level information, in which respondents were queried on basic demographic information (i.e. age, sex, race, marital status, income, and relationship to householder), as well as if they worked at all last week, month and year moved into residence, and their ability to perform everyday tasks and whether they have difficulty hearing, seeing, and concentrating or remembering things. Part 7 data include verification of income to cost when the ratio of income to cost is outside of certain tolerances. Respondents were asked whether they receive help or assistance with grocery bills, clothing and transportation expenses, child care payments, medical and utility bills, as well as with rent payments. Part 8 data include recent mover information, such as how many people were living in last unit before move, whether last residence was a condo or a co-op, as well as whether this residence was outside of the United States.